Applied AI Engineer, Advertising Agents (New Grad)

Are you a recent graduate excited to apply LLMs and Agent technology to real advertising products from day one? Join our advertising team to help build an AI-driven intelligent account hosting and optimization platform. You'll work alongside senior engineers to develop AI advertising expert systems (AI Agents) that diagnose accounts and safely auto-tune delivery in a closed optimization loop.

Your work will directly lower the delivery barrier for global advertisers on our platform. While ensuring the health and compliance of the account ecosystem, your contributions will help maximize advertisers' willingness to spend and platform revenue.

• →Contribute to the In-house AI Account Manager System: Support the design and implementation of components of the "AI Account Manager," including automated hosting, optimization scheduling, and monitoring across concurrently running advertiser accounts.
• →Help Build Intelligent Diagnosis and Strategy Recommendation Models: Work with performance data and delivery history to help identify account health signals and delivery pain points (such as inability to scale volume, sudden drops in ROI, or budget capping); assist in using LLMs to generate actionable, auditable account-level tuning strategies.
• →Support Reliability and Measurable Quality: Help build and maintain observability, dry-run safeguards, and offline/online evaluation pipelines that keep optimization quality measurable and catch regressions before they reach live spend.
• →Collaborate and Learn: Work closely with senior engineers, the platform-side ad delivery algorithm team, and product managers, gaining exposure to how AI account management capabilities translate into commercial impact.

• Education Background: Bachelor's/Master's degree (recently completed or completing within the next 6 months) in Computer Science, Artificial Intelligence, Data Science, or related fields.
• Work Experience: No professional work experience required; relevant internships, research, coursework, or personal projects involving LLMs, agents, or ML are a plus. Exposure to building or experimenting with agentic systems (e.g., through coursework, hackathons, internships, or personal projects). Solid software-engineering fundamentals: ability to write clean, testable code; familiarity with asynchronous/concurrent programming and typed/data-modeled code.
• Core Technical Stack: Foundational understanding of LLMs, RAG, tool-use, and agent orchestration concepts (including MCP-style tool ecosystems), gained through coursework, projects, or self-study. Some hands-on experience with structured/schema-validated LLM outputs and modern model APIs (e.g., through class projects or internships). Familiarity with REST/API integration; exposure to deployment tools such as Docker or Kubernetes is a plus. Solid grounding in machine learning and AI fundamentals. Strong analytical skills and eagerness to learn complex problem-solving in a production environment.

• Internship or research experience involving large language models, agent-based systems, or recommendation/ads systems.
• Exposure to advertising or recommendation system concepts and metrics (CPA, ROAS, CVR, CTR, conversions).
• Personal or academic projects demonstrating initiative in building AI agents or automation tools.